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A Systematic Review on Literature-based Discovery
- Source :
- ACM Computing Surveys. 52:1-34
- Publication Year :
- 2019
- Publisher :
- Association for Computing Machinery (ACM), 2019.
-
Abstract
- The vast nature of scientific publications brings out the importance of Literature-Based Discovery (LBD) research that is highly beneficial to accelerate knowledge acquisition and the research development process. LBD is a knowledge discovery workflow that automatically detects significant, implicit knowledge associations hidden in fragmented knowledge areas by analysing existing scientific literature. Therefore, the LBD output not only assists in formulating scientifically sensible, novel research hypotheses but also encourages the development of cross-disciplinary research. In this systematic review, we provide an in-depth analysis of the computational techniques used in the LBD process using a novel, up-to-date, and detailed classification. Moreover, we also summarise the key milestones of the discipline through a timeline of topics. To provide a general overview of the discipline, the review outlines LBD validation checks, major LBD tools, application areas, domains, and generalisability of LBD methodologies. We also outline the insights gathered through our statistical analysis that capture the trends in LBD literature. To conclude, we discuss the prevailing research deficiencies in the discipline by highlighting the challenges and opportunities of future LBD research.
- Subjects :
- 0303 health sciences
General Computer Science
Process (engineering)
Computer science
05 social sciences
Timeline
Scientific literature
Data science
Knowledge acquisition
Theoretical Computer Science
Literature-based discovery
03 medical and health sciences
Workflow
Knowledge extraction
Statistical analysis
0509 other social sciences
050904 information & library sciences
030304 developmental biology
Subjects
Details
- ISSN :
- 15577341 and 03600300
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- ACM Computing Surveys
- Accession number :
- edsair.doi...........25e17d9a6ed9cf8eb17df2c702b0c576
- Full Text :
- https://doi.org/10.1145/3365756